A patient's healthcare information tends to be fragmented across multiple, disparate electronic medical record systems (EMR systems). Oftentimes this is because patients visit multiple medical facilities, each having its own EMR system, to meet their different healthcare needs. For instance, a patient may have an EMR with his or her primary care physician, but this EMR may not be shared with any other medical facilities that are involved in treating the patient, such as an emergency room, a specialist, etc. Instead, the patient would have an EMR with each of the other medical facilities. The information in each of the EMRs may be structured differently depending on the characteristics of the EMR system. The result is that patient information is not typically shared between different facilities, which may impair clinicians in their treatment of patients, as clinicians are unable to see the full scope of the patient's condition.
Even within the confines of a single EMR at a single facility, the information may be split between different data models or tables. Combinations of data from these different models are often needed to create a complete view of a patient's state and prognosis. This view is typically created by repeatedly querying the separate data models in an attempt to generate a cohesive view of the information. This can consume significant resources of the underlying system and introduces latency between when the data was modified and the corresponding view updated.
Further, the way EMR systems structure the underlying medical records may not be optimal for all clinical applications. Some EMRs are structured using a relational model, while other EMRs are structured in a hierarchical model. No single model is ideal for all possible query patterns. For example, a lab result may be queried as part of a flowsheet showing the current status of a patient, as part of a broader timeline showing the history of the patient, grouped with similar tests to show a recent trend, or searchable by some arbitrary criteria. The structure of the data in the EMR will not be optimal for all these different query patterns, meaning that some clinical applications may be prohibitively expensive to generate.